From Rankings to Regional Insight: Introducing RankmyAI AI Ecosystem Maps

From Performance Metrics to Spatial Context

At RankmyAI, our rankings have always focused on traction. Through use case specific rankings, special rankings, and country rankings, we identify which AI tools and companies are gaining measurable visibility based on objective indicators such as website traffic. These rankings answer a key question: who is gaining momentum?

However, they do not answer another question that is equally important for understanding the development of AI: where is AI happening?

To address that gap, we are introducing AI Ecosystem Maps. This new feature adds a geographic dimension to our existing data infrastructure. By visualizing AI tools and companies on an interactive map, we move beyond performance benchmarking toward ecosystem analysis.

Why Location Matters in AI Development

Innovation is rarely evenly distributed. AI development tends to cluster around universities, research institutions, urban innovation districts, and regions with active policy support. By plotting companies geographically, we can identify regional hotspots, detect clustering effects, observe urban concentration patterns, and better understand how local ecosystems evolve.

This spatial perspective provides additional context that performance metrics alone cannot offer. It allows stakeholders to see not just which companies are growing, but how AI innovation is structured within and across regions.

Methodological Differences Between Rankings and Maps

There is an important methodological distinction between our rankings and our ecosystem maps. Rankings include only AI tools and companies with measurable traffic. They are designed to benchmark traction and visibility.

The maps, by contrast, allow for flexible inclusion criteria. Depending on the analytical objective, we can display all identified AI tools and companies within a defined geographic area, regardless of their current scale or traffic levels. This provides a comprehensive ecosystem overview that captures early stage ventures, niche players, and emerging initiatives that may not yet meet ranking thresholds but are nevertheless part of the regional innovation landscape.

At the same time, the maps can also be configured to display a curated selection, such as the top performing tools within a specific region. This makes it possible to combine geographic context with performance filtering, enabling both ecosystem wide analysis and focused benchmarking within a defined territory.

Together, rankings and maps provide complementary perspectives: performance and presence, scale and structure.

Showcase: Province of Groningen

Our first showcase map focuses on the Province of Groningen in the Netherlands. The province currently hosts more than 80 AI tools and companies, with the majority located in the city of Groningen.

While only a subset of these companies appears in our monthly ranking due to traffic requirements, the map reveals the broader foundation of the local ecosystem. It shows density, proximity, and concentration patterns that rankings alone cannot convey. The geographic distribution highlights how regional ecosystems often concentrate around academic and innovation infrastructure.

Use Cases for Policymakers, Researchers, and Investors

AI Ecosystem Maps are relevant for a wide range of stakeholders.

For regional governments and economic development agencies, the maps can inform funding strategies, cluster development initiatives, and innovation support programs. They provide a structured overview of where AI activity is concentrated and where potential gaps may exist.

Universities can use ecosystem maps to visualize spin offs and research commercialization pathways. Investors can identify geographic concentrations of early stage AI ventures that may not yet be widely visible. Researchers can combine geographic data with performance metrics to analyze spatial patterns in AI development and adoption. Media and ecosystem builders can use the maps to strengthen regional positioning and storytelling.

Building a Global Library of AI Ecosystems

The introduction of ecosystem maps builds on RankmyAI’s global database of more than 61,000 AI tools and companies. By adding a geographic dimension, we enable local ecosystem benchmarking, regional comparison, and a more structured understanding of how AI ecosystems evolve over time.

The maps can be embedded on external websites and used as dynamic overviews of local AI activity. In the coming weeks, we will roll out additional showcase maps for other cities, provinces, and countries. Our ambition is to create a growing library of AI ecosystem maps that complements our rankings and country reports, offering a more complete view of the global AI landscape.

With rankings, we measure traction. With maps, we reveal structure. Together, they provide a more comprehensive and data driven understanding of how and where AI ecosystems are forming.

If you are interested in developing an AI ecosystem map for your city, region, or country, we invite you to contact us to explore the possibilities.